Breast Contour Extraction and Pectoral Muscle Segmentation in Digital Mammograms

Report as inappropriate Your report has been received

Description

The International Journal of Computer Science and Information Security (IJCSIS Vol. 9 No. 2) is a reputable venue for publishing novel ideas, state-of-the-art research results and fundamental advances in all aspects of computer science and information & communication security. IJCSIS is a peer reviewed international journal with a key objective to provide the academic and industrial community a medium for presenting original research and applications related to Computer Science and Information Security. . The core vision of IJCSIS is to disseminate new knowledge and technology for the benefit of everyone ranging from the academic and professional research communities to industry practitioners in a range of topics in computer science & engineering in general and information & communication security, mobile & wireless networking, and wireless communication systems. It also provides a venue for high-calibre researchers, PhD students and professionals to submit on-going research and developments in these areas. . IJCSIS invites authors to submit their original and unpublished work that communicates current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world information security problems.

Shared by: ijcsis

Stats

views:: 256
posted:: 3/8/2011
language:: English
pages:: 7

Public Domain

Document Sample

(IJCSIS) International Journal of Computer Science and Information Security,

Breast Contour Extraction and Pectoral Muscle
Segmentation in Digital Mammograms

Arun Kumar M.N H.S. Sheshadri
Research Scholar, Department of Electronics and Department of Electronics and Communication
Communication Engineering Engineering
P.E.S. College of Engneering P.E.S. College of Enginering
Mandya, India Mandya, India
akmar_mn11@rediffmail.com hssheshadri@hotmail.com

Abstract— Breast cancer is one of the major causes of fatality systems are quite high, the false positive detection rates are
among women aged above 40. Digital mammography is used by also high. Accordingly, work continues on improving all
radiologists for analysis and interpretation of cancer. Visual aspects of computer-aided detection (CAD) for
reading and interpretation of mammograms is a very demanding mammography. Implementation of breast border detection,
and expensive job. Even well-trained experts may have an
interobserve variation rate of 65-75 percent. Extraction of the
because of some factors such as the low contrast near the
breast contour and pectoral muscle segmentation is necessary in borders, image noise and artifacts is complicated.
order to limit the search for abnormalities by Computer Aided
Diagnosis (CAD). A new technique for breast border extraction In mammogram, image processing [27-31] and computer-
and pectoral muscle segmentation is explored in this paper. The aided diagnosis of breast cancer breast segmentation is an
technique is applied to 250 MIAS mammograms. This method important pre-processing step. The accuracy and efficiency of
has given about 98% in segmenting the pectoral muscle. processing algorithms will be increased if the processing is
limited to a specific target region in an image.
Keywords –Image Processing, mammography, morphology, filter,
edge detection.
Extracting the pectoral muscle [23, 24, 25] is particularly
important in automated mammogram image assessment.
I. INTRODUCTION Segmentation of the pectoral muscle is a non-trivial, complex
and demanding task. It is also complicated further by a
One of the leading causes of death among women is the number of factors. Foremost thing is, the muscle edge is not a
breast cancer. Early diagnosis and subsequent treatment can straight line, but can be convex, concave or a mixture of both.
significantly improve the chance of survival for patients with Secondly muscle edge though may appear to be visually
breast cancer. Most effective method for the detection of early continuous; the edge exhibits variations in texture and
breast cancer is mammography. Mammograms are among the sharpness. This paper describes a new technique for extracting
most difficult radiological images to interpret by radiologists. the breast border and segmenting the pectoral muscle of digital
Studies have shown that radiologists do not detect all breast mammograms.
cancers that are retrospectively detected on the mammograms.
Detection is the ability to identify potential abnormalities, The remainder of this paper is organized as follows. In
such as microcalcification, masses, and architectural Section 2, the approaches to extraction of breast border and
distortions. Diagnosis is the ability to characterize or classify segmentation of pectoral muscle are described. The theory and
a detected abnormal entity as being either benign or malignant. proposed techniques are presented in Section 3. Experimental
However, before CADe algorithms can perform their task of results are given and discussed in Section 4. Finally, the paper
identifying suspicious regions in a mammogram, a series of is summarized in Section 5.
pre-processing steps must be taken. These include:
mammogram orientation, label and artifact removal, II. PREVIOUS APPROACHES TO BREAST BORDER
mammogram enhancement, breast contour detection and EXTRACTION AND PECTORAL MUSCLE
pectoral muscle segmentation SEGMENTATION

Many computer algorithms [1, 2, 3] have been proposed There have been various approaches to the task of
for automating various aspects of detecting the presence of isolating the breast region.
cancer in mammograms. While detection rates for automatic

53 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,

M. Wirth et al. developed an algorithm [1] that uses [19] are implemented on a number of mammogram images by
morphological preprocessing and fuzzy rule-based algorithm Ayman et.al. The segmentation outputs of these methods were
for breast region extraction. Kostas Marias et al. [2] used the very efficient and excellent. Method proposed in [20] applies
boundary extraction technique based on a combination of the the meta-heuristic methods such as Ant Colony Optimization
Hough transform followed by image gradient operators and (ACO) and Genetic Algorithm (GA) for identification of
morphology in order to make coherent the breast region part of suspicious region in mammograms.
the image. Histogram equalization and thresholding process
are employed by Barba J. Leiner et al. [3] to extract only the
There have been various approaches to the task of
region of the image that corresponds to the breast.
segmenting the pectoral muscle.
Segmentation of the breast region in mammograms has
traditionally been achieved using methods besides active A histogram-based thresholding technique is used by K.
contours [4]. Semmlow et al. [5] used a spatial filter and Sobel Thangavel and M. Karnan [23] to separate the pectoral muscle
edge detector to locate the breast boundary on region. For selecting the threshold value the global optimum
xeromammograms. Global thresholding has been used in is considered. The intensity values smaller than global
many cases to segment the breast region from the background optimum threshold are changed to zero, and the gray values
[6-7]. The major problem with using global thresholding is the greater than the threshold are changed to one. To better
nonuniform background region, although efforts, such as that preserve the pectoral muscle region erosion and dilation
of Masek et al. [8] using local thresholding have shown more operations are applied. To segment the pectoral muscle region
promise. the gray level mammogram image is converted to binary
image. The white pixels in the lower left corner of the
A system of masking images with different thresholds to mammogram image indicate the pectoral muscle region.
find the breast edge is developed by Abdel-Mottaleb et al. [9].
Gradient based method is proposed by Méndez et al. [10] to Kwork et al. [24] developed a method for automatic
find the breast contour. They used a two level thresholding pectoral muscle segmentation on mammograms by straight
technique to isolate the breast region of the mammogram. The line estimation and cliff detection. A straight line estimates the
smoothed mammogram is divided into three regions and then muscle edge and cliff detection refines the detected edge by
a tracking algorithm is applied to the mammogram to detect surface smoothing and edge detection in a restricted
the border. Bick et al. [11] proposed a global segmentation neighborhood.
approach that incorporates aspects of thresholding, region
growing and morphological filtering. Lou et al. [12] proposed H. Mirzaalian et al. developed [25] a new method for the
a method based on the assumption that the trace of intensity identification of the pectoral muscle in MLO mammograms.
values from the breast region to the air-background is a The developed method is based on nonlinear diffusion
monotonic decreasing function. algorithm. They compared their results by those recognized by
two expert radiologists. To evaluate the accuracy of proposed
One of the inherent limitations of these methods is the method, HDM (Hausdorff Distance Measure) and MAEDM
fact that very few of them preserve the skin or nipple. The (Mean of Absolute Error Distance Measure) were used.
most promising method of extracting the breast contour
focuses on modeling the non-breast region of a mammogram R.J. Ferrari proposed [26] a new method for the
using a polynomial method, as described by Chandrasekhar identification of the pectoral muscle in MLO mammograms
and Attikiouzel [13, 14]. based upon a multiresolution technique using Gabor wavelets.
This new method overcomes the limitation of the straight-line
Maysam Shahedi et al. proposed a new algorithm [15] for representation considered in their initial investigation. The
automatic breast border detection in digital mammograms results of the Gabor-filter-based method indicated low
based on local adaptive thresholding method. Roshan Hausdorff distances with respect to the hand-drawn pectoral
Dharshana Yapa et.al. presented a new algorithm [16] for muscle edges.
estimating skin-line and breast segmentation using fast
marching algorithm. They introduced some modifications to Mario Mustra et al. [17] uses wavelet decomposition,
the traditional fast marching method, specifically to improve image blurring and edge detection using the Sobel filter for
the accuracy of skin-line estimation and breast tissue breast border detection and pectoral muscle segmentation. N.
segmentation. Nicolau et al. [34] proposed the use of Independent
Component Analysis (ICA) for identification and subsequent
The method proposed in [17] initially determines removal of the pectoral muscle.
intensity value of the background to be able to find pixels that
create the border line. Then breast centre has been taken as III. PROPOSED BREAST BORDER EXTRACTION AND
the starting point for a simple region growing algorithm. H. PECTORAL MUSCLE SEGMENTATION TECHNIQUE
Mirzaalian et al. proposed an algorithm [18] based on
polynomial modeling to detect breast contour. Two methods

54 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,

The block diagram for pectoral muscle segmentation is
shown in Fig. 1. Short description of each block is given.

Mammogram input
(a) (b)

Breast Border Detection Figure 2: Results for MIAS image mdb003 (a). Original image; (b). Artifacts
removed in the mdb003

Edge Detection and Filtering Techniques
Locate the Region Containing the Pectoral Muscle
This step uses the Sobel edge detector followed by
dithering and 2-D order statistic filtering. The Sobel method
finds edges using the Sobel approximation to the derivative.
Wavelet Decomposition Edge detection is followed by dithering. A logical OR
operation is done on dithered and edge detected image. A 2-D
order static filtering is applied on the image obtained as a
result of the previous steps. The result for mdb003 is shown in
Fig. 3 after applying these steps.

Mammogram with Pectoral Muscle Segmentation

Figure 1: Steps carried out for pectoral muscle segmentation.

3.1 Breast Border Detection
(a) (b) (c)
We explored a new technique for breast region
segmentation using morphological and filtering techniques.
The steps followed to detect the breast border involves: - Figure 3: Results for MIAS image mdb003 (a). Edge detection; (b). Dithering
Removal of noise by median filter, Artifacts removal by ; (c). 2-D statistic filtering
morphological operation, Edge detection using Sobel method,
filtering, finding the perimeter of the binarized image and thus Multidimensional image filtering
detect the breast border.
This step removes the noises using a multidimensional
Removal of Noise image filtering. A rotationally symmetric Gaussian low pass
filter filters the image. After that the image is converted to
Median filter is used to remove the noise. It is the binary image and erosion is carried out. Fig. 4 shows the
nonlinear filter used to remove the impulsive noise from an results for MIAS image mdb003 after applying these steps.
image. Median filter is a spatial filtering operation. In the
proposed median filter output pixel contains the median value
in the 3X3 neighborhood around the corresponding pixel in
the input image.

Artifacts Removal

The original mammogram is opened by using a suitable
structuring element. After the opening of mammogram it is Figure 4: Results for MIAS image mdb003
reconstructed. Next step is to threshold the difference image
with 102, which is experimentally obtained. Finally Find perimeter pixels in binary image and superimpose on the
morphological operators are applied to smooth irregularities original image
and expand region. Fig. 2 shows the results of these steps on
MIAS image mdb003. Finally the perimeter pixels in binary image are found.
This perimeter is the boundary of the breast image. Fig. 5

55 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,

shows the results. A pixel is the part of the perimeter if it is Now a line FG is drawn parallel to the line BD through E. It
nonzero and it is connected to at least one zero-valued pixel. can be seen that for all the 250 images the reduced rectangle
The connectivity used is 8. AFGD still include the pectoral muscle. Fig. 8 shows this
result for mdb016.

Figure 5: Contour superimposed on original image mdb003.

3.2 Locate the region containing the pectoral muscle

Pectoral muscle detection is a challenging task in the
Figure 8: The reduced area that containing the pectoral muscle region is
breast segmentation process. The algorithm for pectoral
enclosed in AFGD.
muscle segmentation proposed in this paper consists of few
steps. Technique for segmenting pectoral muscle presented in
this paper uses wavelet decomposition, and edge detection
3.3 Wavelet decomposition
using the Canny filter.
Wavelet decomposition of fourth level is being done.
The region of interest containing pectoral muscle is
Fourth level wavelet decomposition gives the best results for
determined by two steps. First a rectangle which encloses the
detecting larger structures, such as pectoral muscle. The fourth
pectoral muscle is determined and then a refinement/reduction
level decomposition gives the best results because it preserves
to this rectangle is done so that the processing time for
enough rough details while at the same time remove fine
pectoral muscle segmentation can be still reduced. The initial
details like noise and granulation. In this paper, a Daubechies
rectangle is formed by three points A B and C. For example, if
filter has been used. Daubechies wavelets are a family of
the image shows MLO view of the right breast, the first point
orthogonal wavelets defining a discrete wavelet transform and
A is top left corner of the image with coordinates (1,1). The
characterized by a maximal number of vanishing moments for
second point B is determined by the contour of skin-air
some given support. With each wavelet type of this class, there
interface. The third point C is chosen to be approximately at
is a scaling function which generates an orthogonal
half of image height. By those three points a rectangle is
multiresolution analysis. Fig 9 shows a Daubechies 20 2-d
determined. Fig. 7 shows the breast contour superimposed on
wavelet.
the image mdb016 and the rectangle ABCD determined.

Figure 7: Breast contour superimposed on the image mdb016 and the
rectangle ABCD determined.
Figure 9 : Daubechies 20 2-d wavelet

The reason to reduce the size of the rectangle is to reduce
After the wavelet decomposition edges that were detected
the processing time for pectoral muscle segmentation and is
by the Canny filter inside the pectoral muscle region are
done in the following way. A new point E is determined on the
removed by approximating muscle boundary with a straight
breast contour in such a way that point E on the breast contour
line that connects upper right corner and lower left corner of
has a maximum distance from the line BD towards point A.
muscle region in the case of the right breast image.

56 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,

Some of the results of the proposed method for pectoral
muscle identification is explained below. Fig. 12 shows the
IV. EXPERIMENTAL RESULTS successful results of the proposed method.

The proposed method applied to 250 mammograms from
Mammography Image Analysis Society (MIAS) database [21].
The various results obtained are discussed below. Evaluation
of breast contour detected in the mammograms was performed
by the Hausdorff Distance Measure (HDM) [22] and also the
Mean of Absolute Error Distance Measure (MAEDM).
Evaluation is based on a distance transforms and image
algebra between the edges identified by radiologists and by
proposed method. The accuracy of contour detection is 99.06.
(a) (b) (c)
Some of the results of the proposed method for breast
contour extraction are explained below. Fig. 10 shows the
successful results of the proposed method. Fig. 11 shows the
failure case.

(d) (e)

Figure 12: Pectoral muscle identification results for MIAS image mdb016.
(a).Breast contour superimposed on original image; (b). The region of interest
that contain the pectoral muscle; (c). Segmented area that contain the pectoral
(a) (b) (c) muscle; (d). Wavelet decomposed image; (e). Pectoral muscle edge identified
on image.

V. CONCLUSION.

In this paper a method for the detection of the breast
contour and pectoral muscle segmentation is presented. The
(d) proposed method for detecting the breast border contour is
Figure 10: Mammogram segmentation results for MIAS image mdb016. (a). tested on the 250 MIAS datasets. This method gave 99.06
Original Mammogram; (b). Noise & Artifacts removal after filtering and successes in detecting the correct skin-air interface. The
morphological operation. (c). Binary Image; (d). Contour superimposed on
proposed method fails in detecting the correct skin-air
original.
interface for very few mammograms because of the noise (big
size artifacts). Advantage of this method is low algorithm
complexity and therefore short processing time. Our further
development concerns smoothing of the breast border and
pectoral muscle segmentation line. The proposed technique is
fully autonomous, and is able to preserve the skin and nipple.

Pectoral muscle detection is a challenging task because it
is not very well differenced from the surrounding breast tissue.
There is different intensity variation of the pectoral muscle
and the surrounding tissue for each mammogram images. The
(a) (b) (c) method proposed in this paper uses wavelet decomposition.
This approach works well with an accuracy of 98% because
Figure 11: Mammogram segmentation results for MIAS mdb012. (a). Original pectoral muscle is rather large object for detection. Future
Mammogram; (b). Image after removal of artifacts; (c) Contour work will focus on smoothening the breast contour and
superimposed on original image. pectoral muscle edge.

57 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,

REFERENCES
[16] Roshan Dharshana Yapa, and Koichi Harada, “Breast Skin-Line
Estimation and Breast Segmentation in Mammograms using Fast-Marching
[1] M. Wirth, D. Nikitenko, and J. Lyon, “Segmentation of the Breast Region
Method”, International Journal of Biological and Medical Sciences 3:1 2008
in Mammograms using a Rule-Based Fuzzy Reasoning Algorithm”, GVIP
Special Issue on Mammograms, 2007
[17] Mario Mustra, Jelena Bozek, and Mislav Grgic, “Breast Border
Extraction And Pectoral Muscle Detection Using Wavelet Decomposition”,
[2] Kostas Marias, Christian Behrenbruch, Santilal Parbhoo, Alexander
978-1-4244-3861-7/09/ ©2009 IEEE, pp. 1428-1435.
Seifalian, and Michael Brady, “A Registration Framework for the Comparison
of Mammogram Sequences” , IEEE TRANSACTIONS ON MEDICAL
IMAGING, VOL. 24, NO. 6, JUNE 2005
[18] H. Mirzaalian, M. R. Ahmadzadeh, and F. Kolahdoozan, “Breast Contour
Detection on Digital Mammogram”, 0-7803-9521-2/06/ @ 2006 IEEE, pp.
[3] Barba J. Leiner, Vargas Q. Lorena, Torres M. Cesar, and Mattos V.
1804-1808.
Lorenzo “Microcalcifications Detection System through Discrete Wavelet
Analysis and Contrast Enhancement Techniques” Electronics, Robotics and
[19] Ayman A. AbuBaker, R.S.Qahwaji, Musbah J. Aqel, and Mohmmad H.
Automotive Mechanics Conference 2008
Saleh, “Average Row Thresholding Method for Mammogram Segmentation”,
Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th
[4] Michael A. Wirth, and Alexei Stapinski, “Segmentation of the Breast
Annual Conference Shanghai, China, September 1-4, 2005
Region in Mammograms using Active Contours”,
http://www.uoguelph.ca/~mwirth
[20] K.Thangavel, and M.Karnan, “Computer Aided Diagnosis in Digital
Mammograms: Detection of Microcalcifications by Meta Heuristic
[5] Semmlow J.L, Shadagopappan A, Ackerman L.V, Hand W, and Alcorn
Algorithms “,GVIP Journal, Volume 5, Issue 7, July 2005
F.S, “A Fully Automated System for Screening Xeromammograms”,
Computers and Biomedical Research, 13. Pp.350-362, 1980.
[21] J. Suckling, J. Parker, D. R. Dance, S. Astely, I. Hutt, C. R. M. Boggis, I.
Ricketts, E. Stamakis, N. Cerneaz, S. L. Kok, P. Taylor, D. Betal, and J.
[6] Lau T.K, and Bischof W.F, “Automated Detection of Breast Tumors
Savage, "The Mammographic Image Analysis Society Digital Mammogram
using the Asymmetry Approach”, Computers and Biomedical Research, 24,
Database," in Digital Mammography: Proc. of the 2nd International Workshop
pp.273-295, 1991.
on Digital Mammography, York, England: Elsevier, 1994, pp. 375-378.
[7] Yin, Giger M.L, Doi K, Metz C.E, Vyborny C.J, and Schmidt R.A,
[22] D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, "Comparing
“Computerized Detection of Masses in Digital Mammograms: Analysis of
Images using the Hausdorff Distance," IEEE Trans. Pattern Anal. Machine
Bilateral Subtraction Images”, Medical Physics, 18, pp.955-963, 1991.
Intell., vol. 15, 1993, pp. 850-863.
[8] Masek M, Attikiouzel Y, and deSilva, C.J.S, “Skin-air interface
Extraction from Mammograms using an Automatic Local Thresholding
[23] K. Thangavel, and M.Karnan, “ Computer Aided Diagnosis in Digital
Algorithm”, in 15th Biennial International Conference Biosignal, Brno, Czech
Mammograms: Detection of Microcalcification by Meta Heuristic
Republic, pp.204-206, 2000.
Algorithms”, GVIP Journal, Volume 5, Issue 7,July 2005.
[9] Abdel-Mottaleb M, Carman C.S, Hill C.R., and Vafai, S., “Locating the
[24] S.M. Kwok, R. Chandrashekar, and Y. Attikkiouzel, “Automatic
Boundary between the Breast Skin Edge and the Background in Digitized
Pectoral Muscle Segmentation on Mammograms by Straight Line Estimation
Mammograms”, in 3rd International Workshop on Digital Mammography,
and Cliff Detection”, 7th Australian an New Zealand Intelligent Information
Chicago, Illinois, 98, pp.467-470, 1996.
Systems Conference 18-21 November 2001, Perth, Western Australia.
[10] Mendez A.J, Tahoces P.G, Lado M.J, Souto M, Correa J.L, and Vidal
[25] H. Mirzaalian, M.R. Ahmedzadeh, and S. Sadri, “ Pectoral Muscle
J.J, “Automatic Detection of Breast Border and Nipple in Digital
Segmentation on Digital Mammograms by Nonlinear Diffusion Filtering”, 1-
Mammograms”, Computer Methods and Programs in Biomedicine, 49,
4244-1190-4/07/ ©2007 IEEE, pp. 581-584.
pp.253-262, 1996.
[26] R. J. Ferrari, R. M. Rangayyan,, J. E. L. Desautels, R. A. Borges, and A.
[11] Bick U, Giger M.L, Schmidt R.A, Nishikawa R.M, Wolverton D.E, and
F. Frère, “ Automatic Identification of Pectoral Muscle in Mammograms”,
Doi K, “Automated Segmentation of Digitized Mammograms”, Academic
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23, NO. 2,
Radiology, 2, pp.1-9, 1995.
FEBRUARY 2004
[12] Lou S.L, Lin H.D, Lin K.P, and Hoogstrate, “Automatic Breast Region
[27] Sheshadri H.S, and Kandaswamy A, “Detection of Breast Cancer Tumor
Extraction from Digital Mammograms for PACS and Telemammography
Applications”, Computerized Medical Imaging and Graphics, 24, pp.205-220, based on Morphological Watershed Algorithm”, GVIP, 2005, pp. 17-21.
2000.
[28] Sheshadri H.S, and Kandaswamy A, “Experimental Investigation on
[13] Chandrasekhar R, and Attikiouzel Y, “Automatic Breast Border Mammogram Segmentation for Early Detection of Breast Cancer”, Journal of
Segmentation by Background Modeling and Subtraction”, in 5th International Computerized Medical Imaging and Graphics, Elsevier science Vol. 31, 2005,
Workshop on Digital Mammography, Medical Physics Publishing, Toronto, 46-48
Canada, pp.560-565, 2000.

[14] Chandrasekhar R, and Attikiouzel Y, “Gross Segmentation of
Mammograms using a Polynomial Model”, in International Conference of the [29] Sheshadri H.S. and Kandaswamy A, “Mammogram Image Analysis
IEEE Engineering in Medicine and Biology Society, Amsterdam, Netherlands, using Recursive Watershed Algorithm”, National Journal of Technology, Vol.
3, pp.1056-1058, 1996. 1, No. 1, 2004, pp. 73-77.

[15] Maysam Shahedi B K, Rassoul Amirfattahi, Farah Torkamani Azar and [30] Sheshadri H.S, and Kandaswamy A, “Computer Aided Decision System
Saeed Sadri, ”Accurate Breast Region Detection In Digital Mammograms for Early Detection of Breast Cancer”, Indian Journal of Medical research,
Using A Local Adaptive Thresholding Method” , Eight International Vol. 124, 2006, pp. 149-154.
Workshop on Image Analysis for Multimedia Interactive
Services(WIAMIS'07)

58 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,

[31] N. Nicolaou, S. Petroudi, J. Georgiou, M. Polycarpou, and M. Brady,
“Digital Mammography: Towards Pectoral Muscle Removal via Independent
Component Anlysis”, Department of Electrical and Computer Engineering,
Dr. H.S. Sheshadri is working as a Professor in the
University of Cyprus, 1678 Nicosia, CyprusFax. And Wolfson Medical Department of Electronics & Communication Engineering,
Vision Laboratory, Oxford University, Oxford OX2 7DD, UK. PES College of Engineering Mandya, Karnataka. He received
his B.E from University of Mysore in 1980 and Ph.D from
AUTHORS PROFILE PSG Institute of Technology , Coimbatore, Tamilnadu, India.
Arun kumar M.N is a research scholar in PES college of He has published many research papers in International
Engineering, Mandya, Karnataka, India. He graduated from Journals. His research area includes Image Processing, and
Mysore University in Computer Science and Engineering in Computer Vision.
1996. He received his M.Sc(Engg.) from Visvesvaraya
Technological University, Belgaum, Karnataka. His research
interest includes Data Mining, and Image Processing.

59 http://sites.google.com/site/ijcsis/
ISSN 1947-5500